AI Engine Wrapping External Code for Model Reuse
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Solution Overview
Problem
Software developers face challenges in working with artificial intelligence due to complex toolkits, limited APIs, and constrained black-box solutions, making it difficult to effectively utilize AI for real-world problems, and there is a need to make AI more accessible to a broader audience beyond the limited number of data science experts.
Innovation Solution
An AI engine with multiple independent modules on computing platforms that allows for the creation and training of AI models by wrapping external entities of code into software containers, enabling the reuse of pre-trained components and reducing computing cycles, and providing a platform for scalable deployment of AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-trained machine-learning models are reused and wrapped into software containers, then training time and computing cycles are reduced, but the complexity of integrating and managing multiple external code entities increases
Solution Approach 1:
The system segments the AI model development process into independent, reusable modules (external code entities) that can be wrapped into software containers. Each module can be trained and validated separately, then integrated into the final model, reducing overall training time while managing complexity through modular organization.
Solution Approach 2:
The patent introduces an intermediary layer (the software container wrapping mechanism) that mediates between external code entities and the final AI model. This intermediary standardizes interfaces and integration protocols, reducing the complexity of managing multiple external entities while enabling efficient reuse of pre-trained components.
2Adaptability or versatility
If multiple independent modules are used to create and train AI models, then development flexibility and reusability are improved, but the complexity of managing and deploying the system increases
Solution Approach 1:
The patent creates a universal software container framework that can wrap and manage multiple types of external code entities (different programming languages, frameworks, and model types) through standardized interfaces. This universal approach enables flexible reuse of pre-trained models across different applications while simplifying deployment through a common management layer.
Solution Approach 2:
The system performs preliminary actions by pre-training and validating external code entities before integration into the final AI model. These pre-processed modules are wrapped into containers with standardized interfaces, reducing the complexity of real-time integration and deployment while maintaining high flexibility in model composition.
3Adaptability or versatility
If external code entities are wrapped into software containers with standardized interfaces, then interoperability and reusability are improved, but the complexity of creating and maintaining the container infrastructure increases
Solution Approach 1:
The patent creates standardized template containers that can be copied and instantiated for different external code entities. These templates define standard interfaces and integration patterns, enabling rapid reuse of containerized modules across different projects while reducing the complexity of creating new container infrastructure from scratch.
Data Source
AI summary
An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.